class_docstr: Sequential User Matrix Model
  
  :Citation:
  
      Lian, J., Batal, I., Liu, Z., Soni, A., Kang, E. Y., Wang, Y., & Xie, X.,
      "Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations", arXiv preprint arXiv:2102.09211, 2021.
docstr: The main function to create SUM model.
  
  Returns:
      object: The output of SUM section, which is a concatenation of user vector and target item vector.
docstr_tok: ["The","main","function","to","create","SUM","model",".","Returns",":","object",":","The","output","of","SUM","section",",","which","is","a","concatenation","of","user","vector","and","target","item","vector","."]
code: def _build_seq_graph(self):
      """The main function to create SUM model.
  
      Returns:
          object: The output of SUM section, which is a concatenation of user vector and target item vector.
      """
      hparams = self.hparams  # noqa: F841
      with tf.compat.v1.variable_scope("sum"):
          self.history_embedding = tf.concat(
              [
                  self.item_history_embedding,
                  self.cate_history_embedding,
              ],
              2,
          )
          cell = self._create_sumcell()
          self.cell = cell
          cell.model = self
          final_state = self._build_sum(cell)
  
          for _p in cell.parameter_set:
              tf.compat.v1.summary.histogram(_p.name, _p)
          if hasattr(cell, "_alpha") and hasattr(cell._alpha, "name"):
              tf.compat.v1.summary.histogram(
                  cell._alpha.name, cell._alpha
              )
          if hasattr(cell, "_beta") and hasattr(cell._beta, "name"):
              tf.compat.v1.summary.histogram(
                  cell._beta.name, cell._beta
              )
  
          final_state, att_weights = self._attention_query_by_state(
              final_state, self.target_item_embedding
          )
          model_output = tf.concat(
              [final_state, self.target_item_embedding], 1
          )
          tf.compat.v1.summary.histogram("model_output", model_output)
      return model_output
  
file_path: recommenders/recommenders/models/deeprec/models/sequential/sum.py
imports: import tensorflow
  from tensorflow.compat.v1.nn import dynamic_rnn
  from recommenders.models.deeprec.models.sequential.sequential_base_model import SequentialBaseModel
  from recommenders.models.deeprec.models.sequential.sum_cells import SUMCell, SUMV2Cell
label_desc: Use image node_id 1 for calling the SUMModel obj's underlying member method code with example usage: obj._build_seq_graph() and returns: model_output